Analytics systems have the capability to automatically analyze image data from surveillance cameras. Often, the analytics systems will track moving objects against fixed background models. More sophisticated functions include object detection to determine the presence of an object or a type of the object. Even higher level functions include object analysis and recognizing temporal and spatial events associated with the image data. The analytics systems generate video primitives or metadata for the detected objects and determined events, which the analytics systems can further process or send over the data networks to other systems for storage and incorporation into the image data as metadata, for example. These objects of interest are described via a set of “video primitives,” which may be a text description of some or all of the objects and observable features within a video. These video primitives also may include descriptions of the objects, their locations, velocities, shape, colors, location of body parts, etc.
While analytics systems have historically been separate systems apart from the surveillance cameras, the surveillance cameras themselves are increasingly providing this functionality. Integrating the analytics functionality within the cameras themselves has advantages. It eliminates the cost and maintenance associated with deploying a separate analytics system to accomplish the same objective, and enables more efficient analysis by eliminating the messaging overhead associated with sending the image data over the data network for analysis by the separate analytics systems.
It is often required to train or configure these analytics systems to process the image data. Regions of interest such as points, lines and/or areas within the image data can function as virtual tripwires or other bases for analysis of the image data. For example, there might be regions of interest associated with the threshold of a door or point of sale terminal or a portion where customer would form a queue. When the analytics systems detect that objects within the image data have crossed, entered, left, and/or overlapped with regions of interest, the analytics systems generate video primitives in response. The video primitives might be associated with security events or other events of interest that operators of the surveillance camera systems want to identify.
Operators configure the analytics systems by using a separate computer system. Operators typically utilize a graphical user interface (GUI) application of the separate computer system that might provide a graphical drawing tool. The operator can iteratively “draw” the regions of interest upon the displayed scenes from the cameras's fields of view. The operator then sends defined regions of interest to the analytics systems to be used in the image data analysis, and repeats this process for each of the surveillance cameras.